Manage AWS Elastic Load Balancers to distribute traffic across cloud resources. Create, list, or delete load balancers with configuration options for network, application, or classic types.
Manage AWS Elastic Load Balancers
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | ||
| region | Yes | ||
| name | No | ||
| lbType | No | application | |
| scheme | No | ||
| subnets | No | ||
| securityGroups | No | ||
| listeners | No | ||
| healthCheck | No | ||
| tags | No | ||
| targetGroups | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Manage' implies mutation capabilities (create/delete) and read operations (list), but it doesn't disclose critical behavioral traits such as authentication requirements, rate limits, destructive effects of 'delete', or response formats. For a tool with 11 parameters and no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence, 'Manage AWS Elastic Load Balancers', which is front-loaded and wastes no words. It efficiently states the tool's scope without unnecessary elaboration, earning a high score for brevity and clarity in structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (11 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It doesn't provide enough context for an AI agent to understand how to invoke the tool correctly, such as explaining parameter dependencies, action-specific requirements, or expected outcomes. For a multi-action tool with significant input complexity, this is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 11 parameters have descriptions in the schema. The tool description adds no meaning beyond the schema—it doesn't explain what parameters like 'healthCheck', 'listeners', or 'targetGroups' are for, their formats, or how they relate to actions. With low coverage and no compensation in the description, this falls short of the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Manage AWS Elastic Load Balancers' states the resource (AWS Elastic Load Balancers) and a general verb ('Manage'), but it's vague about what specific operations are included. It doesn't distinguish from sibling tools like aws_ec2 or aws_vpc, which also manage AWS resources. The purpose is clear at a high level but lacks specificity about the CRUD operations available.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for choosing between 'list', 'create', or 'delete' actions, or how it differs from other AWS tools in the sibling list. Without such guidance, users must infer usage from the input schema alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.